Out-of-Domain (OOD) intent detection is vital for practical dialogue systems,
and it usually requires considering multi-turn dialogue contexts. However, most
previous OOD intent detection approaches are limited to single dialogue turns.
In this paper, we introduce a context-aware OOD intent detection (Caro)
framework to model multi-turn contexts in OOD intent detection tasks.
Specifically, we follow the information bottleneck principle to extract robust
representations from multi-turn dialogue contexts. Two different views are
constructed for each input sample and the superfluous information not related
to intent detection is removed using a multi-view information bottleneck loss.
Moreover, we also explore utilizing unlabeled data in Caro. A two-stage
training process is introduced to mine OOD samples from these unlabeled data,
and these OOD samples are used to train the resulting model with a
bootstrapping approach. Comprehensive experiments demonstrate that Caro
establishes state-of-the-art performances on multi-turn OOD detection tasks by
improving the F1-OOD score of over $29\%$ compared to the previous best method.

本文提出一种上下文感知的 out-of-domain 意图检测（Caro）框架来模拟多轮对话中的上下文，采用信息瓶颈原理从多轮对话上下文中提取鲁棒特征，并通过多视图信息瓶颈损失去除与意图检测无关的冗余信息，在 Caro 中探索了利用未标记的数据，这种方法提升了 29% 以上的 F1-ODD 分数，实验表明 Caro 在多轮意图检测任务中取得了最好的表现。

基于多轮对话上下文的领域外意图检测

Out-of-Domain Intent Detection Considering Multi-turn Dialogue Contexts

In the wake of a polarizing election, social media is laden with hateful
content. To address various limitations of supervised hate speech
classification methods including corpus bias and huge cost of annotation, we
propose a weakly supervised two-path bootstrapping approach for an online hate
speech detection model leveraging large-scale unlabeled data. This system
significantly outperforms hate speech detection systems that are trained in a
supervised manner using manually annotated data. Applying this model on a large
quantity of tweets collected before, after, and on election day reveals
motivations and patterns of inflammatory language.

使用弱监督的双路径自举方法，基于大规模无标注数据建立在线仇恨言论检测系统，有效解决了标注偏见和标注成本高昂等问题。在选举前、后和当天收集的大量推文上应用该模型，揭示了煽动性语言的动机和模式。